Emergency Control of Vehicle Platoons: System Operation and Platoon Leader Control

1976 ◽  
Vol 98 (3) ◽  
pp. 245-251 ◽  
Author(s):  
L. L. Hoberock ◽  
R. J. Rouse
Keyword(s):  
Discrete Control ◽  
Normal Operation ◽  
Emergency Control ◽  
Car Following ◽  
Stopping Distance ◽  
Preceding Vehicle ◽  
Control Concept ◽  
Modified Block ◽  
Vehicle Platoons ◽  
Vehicle Capacity

In the work herein and in [1], an automatic control concept is developed for both normal and emergency longitudinal regulation of high density strings of vehicles with velocities in the range 50 to 90 mph (80 to 144 km/hr). Under this concept, a vehicle string is formed into platoons of vehicles, the lead vehicles of which are governed by wayside-mounted controllers. Excluding platoon leaders, vehicles are controlled by on-board controllers, termed car-following or following-law controllers, which continuously regulate vehicle spacing and velocity with respect to the preceding vehicle, and velocity with respect to an external, desired velocity. Wayside controllers, which communicate with vehicles only when they cross discrete control points along the guideway, provide “modified block control” of lead vehicles, maintaining a safe stopping distance such that collisions under an instantaneous stop cannot occur between vehicles of separate platoons. The proposed control appears suitable for realistic guideway conditions and provides flexibility in performance tradeoffs among wayside hardware, safety, nominal guideway line speed, and guideway vehicle capacity. Spaces generated between platoons under normal operation afford capability for merging from off-line queues.

Emergency Control of Vehicle Platoons: Control of Following-Law Vehicles

1976 ◽  
Vol 98 (3) ◽  
pp. 239-244 ◽  
Author(s):  
R. J. Rouse ◽  
L. L. Hoberock
Keyword(s):  
Emergency Operation ◽  
Dynamical Analysis ◽  
Normal Operation ◽  
Target Velocity ◽  
Longitudinal Control ◽  
Emergency Control ◽  
Vehicle System ◽  
Design Speed ◽  
Vehicle Platoons

This work presents a dynamical analysis of platooned following-law vehicles under longitudinal control proposed in [1]. It is shown that controller gains selected for normal operation give inadequate performance in emergency operation. Dangerous spacing in platoons moving at lower than design speed and delayed target velocity update effects are investigated. Stability of the vehicle system in emergency operation is related to controller gains, and simulations for various emergency contingencies are presented.

scholarly journals A driver’s car-following behavior prediction model based on multi-sensors data

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Hui Wang ◽  
Menglu Gu ◽  
Shengbo Wu ◽  
Chang Wang
Keyword(s):  
Prediction Model ◽  
Warning System ◽  
Normal Operation ◽  
Support Vector ◽  
Area Network ◽  
Behavior Prediction ◽  
Accelerator Pedal ◽  
Car Following ◽  
Preceding Vehicle ◽  
Time Distance

AbstractThe prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver’s car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

scholarly journals Driver Characteristics Oriented Autonomous Longitudinal Driving System in Car-Following Situation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6376
Author(s):  
Haksu Kim ◽  
Kyunghan Min ◽  
Myoungho Sunwoo
Keyword(s):  
Driving Behavior ◽  
Cruise Control ◽  
Driver Assistance ◽  
Collision Warning ◽  
Driving System ◽  
Traffic Jam ◽  
Car Following ◽  
Preceding Vehicle ◽  
Planning Algorithm ◽  
Control Traffic

Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle.

A new longitudinal car-following control scheme of AVs towards the non-connected situation

2020 ◽  
Vol 34 (13) ◽  
pp. 2050135
Author(s):  
Yang Li ◽  
Min Zhao ◽  
Dihua Sun ◽  
Jin Chen ◽  
Weining Liu
Keyword(s):  
Traffic Congestion ◽  
Nonlinear Partial Differential Equation ◽  
Ar Model ◽  
Driving Safety ◽  
Prediction Errors ◽  
Car Following ◽  
V2v Communication ◽  
Control Scheme ◽  
Preceding Vehicle ◽  
The Impact

Connected cooperative driving is known as the promising way to mitigate traffic congestion, enhance driving safety and improve fuel economy. However, before vehicle-to-vehicle (V2V) communication technology became widely applied, vehicles could not always communicate with the front cars due to the uncertainty of vehicle type and communication function. Towards the non-connected situation and making the most of the on-board sensors of the automated vehicles (AVs), an auto-regression (AR) model was adopted to predict the velocity of the preceding vehicle at the next moment, then a new longitudinal car-following control scheme is given from the perspective of cyber physical system to improve the longitudinal following performance. The sufficient condition ensuring a better performance is acquired by local stability analysis and the impact of velocity prediction errors of the AR model is analyzed through a nonlinear partial differential equation. The experiments based on the US-101 dataset and numerical simulations were carried out and the results are in great agreement with the theoretical analysis, which reveals that applying AR model to predicting the velocity of the preceding vehicle at the next moment can improve the car-following performance of AVs without the support of communication devices.

A REVISED STOCHASTIC OPTIMAL VELOCITY MODEL CONSIDERING THE VELOCITY GAP WITH A PRECEDING VEHICLE

2011 ◽  
Vol 22 (09) ◽  
pp. 1005-1014 ◽  
Author(s):  
KEIZO SHIGAKI ◽  
JUN TANIMOTO ◽  
AYA HAGISHIMA
Keyword(s):  
Velocity Model ◽  
Model Parameters ◽  
Fundamental Diagram ◽  
Cellular Automata Model ◽  
Optimal Velocity ◽  
Car Following ◽  
Proposed Model ◽  
Preceding Vehicle ◽  
Long Time ◽  
Car Following Model

The stochastic optimal velocity (SOV) model, which is a cellular automata model, has been widely used because of its good reproducibility of the fundamental diagram, despite its simplicity. However, it has a drawback: in SOV, a vehicle that is temporarily stopped takes a long time to restart. This study proposes a revised SOV model that suppresses this particular defect; the basic concept of this model is derived from the car-following model, which considers the velocity gap between a particular vehicle and the preceding vehicle. A series of simulations identifies the model parameters and clarifies that the proposed model can reproduce the three traffic phases: free, jam, and even synchronized phases, which cannot be achieved by the conventional SOV model.

scholarly journals Young Adult Drivers: Simulated Behaviour in a Car-following Situation

2017 ◽  
Vol 29 (4) ◽  
pp. 381-390 ◽  
Author(s):  
Rafał Jurecki ◽  
Miloš Poliak ◽  
Marek Jaśkiewicz
Keyword(s):  
Young Adult ◽  
Traffic Safety ◽  
Response Times ◽  
Considerable Influence ◽  
Car Following ◽  
Driver Response ◽  
Emergency Braking ◽  
Young Drivers ◽  
Proper Distance ◽  
Preceding Vehicle

This paper provides a description of driver testing in a simulator. As young drivers are more susceptible to collisions, this was done to determine how young drivers behaved in simulated road situations on a motorway. One of the traffic safety concerns is the failure to keep a proper distance from the vehicle in front, which may result in a rearend collision. The tests simulated car-following situations in which the preceding vehicle performed emergency braking. The experiments were conducted for two scenario variants using different distances from the vehicle in front. The drivers could perform the following emergency manoeuvres: braking with steering away or only braking. The driver response times were compared and analysed statistically. The results were used to determine the emergency manoeuvres performed by the drivers in the simulated road situations. The study reveals that the vehicle surroundings may have a considerable influence on the type of emergency manoeuvres and the driver response time. 

scholarly journals Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

2021 ◽  
Vol 13 (4) ◽  
pp. 88
Author(s):  
Xiaoyuan Wang ◽  
Junyan Han ◽  
Chenglin Bai ◽  
Huili Shi ◽  
Jinglei Zhang ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Free Flow ◽  
Traffic Information ◽  
Flow State ◽  
Simulation Framework ◽  
Car Following ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
Vehicle Information

With the application of vehicles to everything (V2X) technologies, drivers can obtain massive traffic information and adjust their car-following behavior according to the information. The macro-characteristics of traffic flow are essentially the overall expression of the micro-behavior of drivers. There are some shortcomings in the previous researches on traffic flow in the V2X environment, which result in difficulties to employ the related models or methods in exploring the characteristics of traffic flow affected by the information of generalized preceding vehicles (GPV). Aiming at this, a simulation framework based on the car-following model and the cellular automata (CA) is proposed in this work, then the traffic flow affected by the information of GPV is simulated and analyzed utilizing this framework. The research results suggest that the traffic flow, which is affected by the information of GPV in the V2X environment, would operate with a higher value of velocity, volume as well as jamming density and can maintain the free flow state with a much higher density of vehicles. The simulation framework constructed in this work can provide a reference for further research on the characteristics of traffic flow affected by various information in the V2X environment.

scholarly journals Car following and lane changing behavior using NGSIM and China data

2019 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Md Mijanoor Rahman ◽  
Mohd. Tahir Ismail ◽  
Majid Majahar Ali
Keyword(s):  
Reproducing Kernel ◽  
Road Traffic ◽  
Reproducing Kernel Hilbert Space ◽  
Driver Behavior ◽  
Trajectory Data ◽  
Maximum Acceleration ◽  
Lane Changing ◽  
Traffic Jam ◽  
Car Following ◽  
Stopping Distance

Road safety is imperative theme because increasing road fatalities deaths in world. Besides road fatalities, traffic jam is increasing, human is frustrated for uncomfortable journey. The roads safety and passengers comfortable of the roadway system are vastly depended on the Car following (CF) and Lane Changing (LC) features of drivers. CF and LC theory describe the driver behavior by following paths in a traffic stream. In this research, researchers have compared to US-101 Next-Generation-Simulation (NGSIM) data with Beijing forth ring road, China freeways real trajectory data by CF and LC models. The CF data has been calibrated with Genetic Algorithm (GA). Reproducing Kernel Hilbert Space (RKHS) is generated the LC beginning and finishing points. Findings revealed that the CF parameters as maximum acceleration, minimum deceleration, free speed, minimum headway and stopping distance percentages of Chinese data are 74.71%, 79.95%, 66.57%, 0.018% and 65.65% respectively of NGSIM data. After completing the comparison, researchers have been found out optimization safety and comfortable acceleration-deceleration and LC beginning-finishing points of driver behavior. Here this analysis generates the driver behavior at real traffic network on the express highways of specific two roads US-101 (NGSIM) data and Chinese freeways data. Since NGSIM data is well simulated so road traffic is more safety and comfortable for journey.

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